Overview

Dataset statistics

Number of variables16
Number of observations152
Missing cells16
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 KiB
Average record size in memory128.8 B

Variable types

Categorical4
Numeric12

Alerts

subject_id has a high cardinality: 151 distinct values High cardinality
bm_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_volume and 8 other fieldsHigh correlation
bm_non-zero_voxels_volume is highly correlated with bm_non-zero_voxels_numberHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_number is highly correlated with bm_non-zero_voxels_number and 4 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_volume is highly correlated with bm_non-zero_voxels_number and 4 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_volumeHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_numberHigh correlation
ifm_overlap_dil_em_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
ifm_overlap_dil_em_non-zero_voxels_volume is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 3 other fieldsHigh correlation
ifm_non-zero_voxels is highly correlated with bm_non-zero_voxels_number and 2 other fieldsHigh correlation
ifm_non-zero_volume is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
pc_ifm_overlap_dil_em_voxels is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_number and 7 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_volume is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 4 other fieldsHigh correlation
pc_ifm_overlap_em_voxels is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
bm_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_volume and 7 other fieldsHigh correlation
bm_non-zero_voxels_volume is highly correlated with bm_non-zero_voxels_numberHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_number is highly correlated with bm_non-zero_voxels_number and 5 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_volume is highly correlated with bm_non-zero_voxels_number and 5 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_volumeHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_numberHigh correlation
ifm_overlap_dil_em_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_number and 8 other fieldsHigh correlation
ifm_overlap_dil_em_non-zero_voxels_volume is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 4 other fieldsHigh correlation
ifm_non-zero_voxels is highly correlated with bm_minus_ifm_non-zero_voxels_before_plugging_number and 1 other fieldsHigh correlation
ifm_non-zero_volume is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
pc_ifm_overlap_dil_em_voxels is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_number and 8 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_volume is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 4 other fieldsHigh correlation
pc_ifm_overlap_em_voxels is highly correlated with bm_non-zero_voxels_number and 6 other fieldsHigh correlation
bm_non-zero_voxels_number is highly correlated with bm_non-zero_voxels_volume and 1 other fieldsHigh correlation
bm_non-zero_voxels_volume is highly correlated with bm_non-zero_voxels_numberHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_before_plugging_volume and 1 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_before_plugging_number and 1 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_volumeHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_numberHigh correlation
ifm_overlap_dil_em_non-zero_voxels_number is highly correlated with pc_ifm_overlap_dil_em_voxels and 3 other fieldsHigh correlation
ifm_overlap_dil_em_non-zero_voxels_volume is highly correlated with pc_ifm_overlap_dil_em_voxels and 1 other fieldsHigh correlation
ifm_non-zero_volume is highly correlated with bm_non-zero_voxels_number and 2 other fieldsHigh correlation
pc_ifm_overlap_dil_em_voxels is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 4 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_number is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 3 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_volume is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 4 other fieldsHigh correlation
pc_ifm_overlap_em_voxels is highly correlated with ifm_overlap_dil_em_non-zero_voxels_number and 3 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_numberHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_volumeHigh correlation
acq is highly correlated with bm_non-zero_voxels_number and 8 other fieldsHigh correlation
bm_non-zero_voxels_number is highly correlated with acq and 10 other fieldsHigh correlation
bm_non-zero_voxels_volume is highly correlated with bm_non-zero_voxels_number and 1 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_number is highly correlated with acq and 7 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_before_plugging_volume is highly correlated with acq and 7 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_number is highly correlated with bm_minus_ifm_non-zero_voxels_before_plugging_number and 4 other fieldsHigh correlation
bm_minus_ifm_non-zero_voxels_after_plugging_volume is highly correlated with bm_minus_ifm_non-zero_voxels_before_plugging_number and 4 other fieldsHigh correlation
ifm_overlap_dil_em_non-zero_voxels_number is highly correlated with acq and 8 other fieldsHigh correlation
ifm_overlap_dil_em_non-zero_voxels_volume is highly correlated with bm_non-zero_voxels_number and 7 other fieldsHigh correlation
ifm_non-zero_voxels is highly correlated with acq and 4 other fieldsHigh correlation
ifm_non-zero_volume is highly correlated with acq and 7 other fieldsHigh correlation
pc_ifm_overlap_dil_em_voxels is highly correlated with acq and 7 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_number is highly correlated with acq and 7 other fieldsHigh correlation
ifm_overlap_em_non-zero_voxels_volume is highly correlated with bm_minus_ifm_non-zero_voxels_after_plugging_number and 6 other fieldsHigh correlation
pc_ifm_overlap_em_voxels is highly correlated with acq and 8 other fieldsHigh correlation
subject_id is uniformly distributed Uniform

Reproduction

Analysis started2022-03-10 16:21:30.558867
Analysis finished2022-03-10 16:21:47.826302
Duration17.27 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

subject_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
sub-ON81734
 
1
sub-ON33827
 
1
sub-ON98806
 
1
sub-ON22299
 
1
sub-ON21834
 
1
Other values (146)
146 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st rowsub-ON01016
2nd rowsub-ON01802
3rd rowsub-ON02693
4th rowsub-ON02747
5th rowsub-ON02811

Common Values

ValueCountFrequency (%)
sub-ON817341
 
0.7%
sub-ON338271
 
0.7%
sub-ON988061
 
0.7%
sub-ON222991
 
0.7%
sub-ON218341
 
0.7%
sub-ON126881
 
0.7%
sub-ON629551
 
0.7%
sub-ON069101
 
0.7%
sub-ON305351
 
0.7%
sub-ON490801
 
0.7%
Other values (141)141
92.8%

Length

2022-03-10T11:21:47.928318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sub-on817341
 
0.7%
sub-on800381
 
0.7%
sub-on980981
 
0.7%
sub-on481901
 
0.7%
sub-on256581
 
0.7%
sub-on139861
 
0.7%
sub-on527331
 
0.7%
sub-on633351
 
0.7%
sub-on870541
 
0.7%
sub-on234831
 
0.7%
Other values (141)141
93.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

acq
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)1.3%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
mprage
94 
fspgr
57 

Length

Max length6
Median length6
Mean length5.622516556
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfspgr
2nd rowmprage
3rd rowmprage
4th rowmprage
5th rowmprage

Common Values

ValueCountFrequency (%)
mprage94
61.8%
fspgr57
37.5%
(Missing)1
 
0.7%

Length

2022-03-10T11:21:48.007767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-10T11:21:48.061069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mprage94
62.3%
fspgr57
37.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bm_non-zero_voxels_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1338717.02
Minimum885337
Maximum1800552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:48.134096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum885337
5-th percentile999671
Q11127846
median1368188
Q31494382.5
95-th percentile1699332
Maximum1800552
Range915215
Interquartile range (IQR)366536.5

Descriptive statistics

Standard deviation219777.3674
Coefficient of variation (CV)0.1641701451
Kurtosis-0.9366436029
Mean1338717.02
Median Absolute Deviation (MAD)171497
Skewness-0.03007507687
Sum202146270
Variance4.830209121 × 1010
MonotonicityNot monotonic
2022-03-10T11:21:48.251372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12293731
 
0.7%
15778371
 
0.7%
14950951
 
0.7%
14217681
 
0.7%
14927631
 
0.7%
11437461
 
0.7%
15949391
 
0.7%
9661801
 
0.7%
10365341
 
0.7%
16994671
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
8853371
0.7%
9280901
0.7%
9599541
0.7%
9638641
0.7%
9661801
0.7%
9683311
0.7%
9875861
0.7%
9964001
0.7%
10029421
0.7%
10268411
0.7%
ValueCountFrequency (%)
18005521
0.7%
17877741
0.7%
17821511
0.7%
17474411
0.7%
17367161
0.7%
17180251
0.7%
17067351
0.7%
16994671
0.7%
16991971
0.7%
16902531
0.7%

bm_non-zero_voxels_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1476178.55
Minimum1181810.375
Maximum1800552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:48.366530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1181810.375
5-th percentile1288179.5
Q11389965.5
median1471091.5
Q31546792.125
95-th percentile1727370.5
Maximum1800552
Range618741.625
Interquartile range (IQR)156826.625

Descriptive statistics

Standard deviation129009.1661
Coefficient of variation (CV)0.08739401217
Kurtosis-0.02106832212
Mean1476178.55
Median Absolute Deviation (MAD)80666.5
Skewness0.3787855371
Sum222902961
Variance1.664336495 × 1010
MonotonicityNot monotonic
2022-03-10T11:21:48.479304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15949391
 
0.7%
1181810.3751
 
0.7%
1292596.6251
 
0.7%
15778371
 
0.7%
13587351
 
0.7%
14950951
 
0.7%
1508505.251
 
0.7%
14927631
 
0.7%
1237627.51
 
0.7%
1392961.3751
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
1181810.3751
0.7%
12228661
0.7%
12283421
0.7%
12291931
0.7%
1237627.51
0.7%
12388801
0.7%
1281414.3751
0.7%
1286633.751
0.7%
1289725.251
0.7%
1292596.6251
0.7%
ValueCountFrequency (%)
18005521
0.7%
17877741
0.7%
17821511
0.7%
1768666.251
0.7%
1755932.751
0.7%
17547021
0.7%
17474411
0.7%
17367161
0.7%
17180251
0.7%
17067351
0.7%

bm_minus_ifm_non-zero_voxels_before_plugging_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)86.1%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean833.5515841
Minimum14
Maximum5155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:48.593151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile41.5
Q177
median124
Q31676.597473
95-th percentile2494.873169
Maximum5155
Range5141
Interquartile range (IQR)1599.597473

Descriptive statistics

Standard deviation1057.470384
Coefficient of variation (CV)1.268632205
Kurtosis1.601670156
Mean833.5515841
Median Absolute Deviation (MAD)72
Skewness1.34422621
Sum125866.2892
Variance1118243.613
MonotonicityNot monotonic
2022-03-10T11:21:48.711430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
743
 
2.0%
613
 
2.0%
1003
 
2.0%
962
 
1.3%
902
 
1.3%
382
 
1.3%
932
 
1.3%
832
 
1.3%
592
 
1.3%
642
 
1.3%
Other values (120)128
84.2%
ValueCountFrequency (%)
141
0.7%
231
0.7%
321
0.7%
32.9742661
0.7%
371
0.7%
382
1.3%
401
0.7%
432
1.3%
491
0.7%
501
0.7%
ValueCountFrequency (%)
51551
0.7%
4300.0546881
0.7%
4064.6809081
0.7%
3740.3073731
0.7%
2975.4265141
0.7%
2881.9855961
0.7%
2541.5935061
0.7%
2520.235841
0.7%
2469.5104981
0.7%
2458.8317871
0.7%

bm_minus_ifm_non-zero_voxels_before_plugging_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct130
Distinct (%)86.1%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean833.5515841
Minimum14
Maximum5155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:48.822057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile41.5
Q177
median124
Q31676.597473
95-th percentile2494.873169
Maximum5155
Range5141
Interquartile range (IQR)1599.597473

Descriptive statistics

Standard deviation1057.470384
Coefficient of variation (CV)1.268632205
Kurtosis1.601670156
Mean833.5515841
Median Absolute Deviation (MAD)72
Skewness1.34422621
Sum125866.2892
Variance1118243.613
MonotonicityNot monotonic
2022-03-10T11:21:48.938670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
743
 
2.0%
613
 
2.0%
1003
 
2.0%
962
 
1.3%
902
 
1.3%
382
 
1.3%
932
 
1.3%
832
 
1.3%
592
 
1.3%
642
 
1.3%
Other values (120)128
84.2%
ValueCountFrequency (%)
141
0.7%
231
0.7%
321
0.7%
32.9742661
0.7%
371
0.7%
382
1.3%
401
0.7%
432
1.3%
491
0.7%
501
0.7%
ValueCountFrequency (%)
51551
0.7%
4300.0546881
0.7%
4064.6809081
0.7%
3740.3073731
0.7%
2975.4265141
0.7%
2881.9855961
0.7%
2541.5935061
0.7%
2520.235841
0.7%
2469.5104981
0.7%
2458.8317871
0.7%

bm_minus_ifm_non-zero_voxels_after_plugging_number
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.0%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
0.0
149 
2.0
 
1
4940.0
 
1

Length

Max length6
Median length3
Mean length3.01986755
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0149
98.0%
2.01
 
0.7%
4940.01
 
0.7%
(Missing)1
 
0.7%

Length

2022-03-10T11:21:49.041107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-10T11:21:49.099418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0149
98.7%
2.01
 
0.7%
4940.01
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

bm_minus_ifm_non-zero_voxels_after_plugging_volume
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.0%
Missing1
Missing (%)0.7%
Memory size1.3 KiB
0.0
149 
2.0
 
1
4940.0
 
1

Length

Max length6
Median length3
Mean length3.01986755
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0149
98.0%
2.01
 
0.7%
4940.01
 
0.7%
(Missing)1
 
0.7%

Length

2022-03-10T11:21:49.162318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-10T11:21:49.220232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0149
98.7%
2.01
 
0.7%
4940.01
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ifm_overlap_dil_em_non-zero_voxels_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean130253.7285
Minimum76863
Maximum186285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:49.294224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum76863
5-th percentile98672
Q1113505.5
median126969
Q3144582.5
95-th percentile168776
Maximum186285
Range109422
Interquartile range (IQR)31077

Descriptive statistics

Standard deviation22128.53298
Coefficient of variation (CV)0.1698879045
Kurtosis-0.2260785896
Mean130253.7285
Median Absolute Deviation (MAD)15490
Skewness0.4101097088
Sum19668313
Variance489671971.8
MonotonicityNot monotonic
2022-03-10T11:21:49.563116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101521
 
0.7%
1206601
 
0.7%
1035471
 
0.7%
1248281
 
0.7%
1581251
 
0.7%
1218071
 
0.7%
1060991
 
0.7%
1253421
 
0.7%
1836561
 
0.7%
1544771
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
768631
0.7%
920091
0.7%
920921
0.7%
956061
0.7%
963901
0.7%
968481
0.7%
969661
0.7%
985951
0.7%
987491
0.7%
994301
0.7%
ValueCountFrequency (%)
1862851
0.7%
1853181
0.7%
1836561
0.7%
1834441
0.7%
1819941
0.7%
1730151
0.7%
1706591
0.7%
1702831
0.7%
1672691
0.7%
1668791
0.7%

ifm_overlap_dil_em_non-zero_voxels_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean144576.1074
Minimum92009
Maximum198088.1233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:49.678335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum92009
5-th percentile110335.5
Q1130136
median142655
Q3157719.3172
95-th percentile183550
Maximum198088.1233
Range106079.1233
Interquartile range (IQR)27583.31721

Descriptive statistics

Standard deviation21031.61652
Coefficient of variation (CV)0.1454709004
Kurtosis0.042009604
Mean144576.1074
Median Absolute Deviation (MAD)13375.45174
Skewness0.1280665963
Sum21830992.22
Variance442328893.5
MonotonicityNot monotonic
2022-03-10T11:21:49.795100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1296511
 
0.7%
1253421
 
0.7%
1836561
 
0.7%
168929.20981
 
0.7%
1544771
 
0.7%
1567181
 
0.7%
1303221
 
0.7%
138221.84641
 
0.7%
1322201
 
0.7%
1167141
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
920091
0.7%
956061
0.7%
969661
0.7%
101151.06081
0.7%
102602.15921
0.7%
1047141
0.7%
1054231
0.7%
1065841
0.7%
1140871
0.7%
1145611
0.7%
ValueCountFrequency (%)
198088.12331
0.7%
195275.55091
0.7%
192779.34291
0.7%
188810.77261
0.7%
186737.71851
0.7%
1862851
0.7%
185279.82571
0.7%
1836561
0.7%
1834441
0.7%
181989.57661
0.7%

ifm_non-zero_voxels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean12007403.25
Minimum9815328
Maximum12515028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:49.909837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9815328
5-th percentile11538540
Q111762481.5
median12107623
Q312284085.5
95-th percentile12413675.5
Maximum12515028
Range2699700
Interquartile range (IQR)521604

Descriptive statistics

Standard deviation378974.5374
Coefficient of variation (CV)0.03156173984
Kurtosis11.05442629
Mean12007403.25
Median Absolute Deviation (MAD)252543
Skewness-2.345342757
Sum1813117890
Variance1.436217 × 1011
MonotonicityNot monotonic
2022-03-10T11:21:50.032354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122628711
 
0.7%
121912891
 
0.7%
123538811
 
0.7%
124291821
 
0.7%
118701331
 
0.7%
124034661
 
0.7%
116919561
 
0.7%
122481601
 
0.7%
118506581
 
0.7%
120793521
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
98153281
0.7%
99763691
0.7%
113730981
0.7%
114228691
0.7%
114543301
0.7%
114702571
0.7%
114771981
0.7%
115154361
0.7%
115616441
0.7%
115861521
0.7%
ValueCountFrequency (%)
125150281
0.7%
125136521
0.7%
124981021
0.7%
124592071
0.7%
124345811
0.7%
124291821
0.7%
124220631
0.7%
124216651
0.7%
124056861
0.7%
124034661
0.7%

ifm_non-zero_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean13470618.09
Minimum9807673
Maximum15886489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:50.149476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9807673
5-th percentile11978559.5
Q112206633
median12360166
Q315597940
95-th percentile15792909
Maximum15886489
Range6078816
Interquartile range (IQR)3391307

Descriptive statistics

Standard deviation1698028.266
Coefficient of variation (CV)0.1260542207
Kurtosis-1.533280169
Mean13470618.09
Median Absolute Deviation (MAD)252543
Skewness0.4227707616
Sum2034063331
Variance2.883299991 × 1012
MonotonicityNot monotonic
2022-03-10T11:21:50.265272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122628711
 
0.7%
121461981
 
0.7%
124034661
 
0.7%
122387631
 
0.7%
152900491
 
0.7%
121912891
 
0.7%
123305881
 
0.7%
122592561
 
0.7%
158450921
 
0.7%
156063851
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
98076731
0.7%
99763691
0.7%
118482461
0.7%
119107981
0.7%
119458551
0.7%
119584811
0.7%
119586571
0.7%
119750321
0.7%
119820871
0.7%
119856451
0.7%
ValueCountFrequency (%)
158864891
0.7%
158450921
0.7%
158411741
0.7%
158240211
0.7%
158222651
0.7%
158190941
0.7%
158158561
0.7%
158021521
0.7%
157836661
0.7%
157812031
0.7%

pc_ifm_overlap_dil_em_voxels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.01084585309
Minimum0.00657400695
Maximum0.01535294718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:50.383015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.00657400695
5-th percentile0.008245660816
Q10.009517963828
median0.0105921169
Q30.01213429552
95-th percentile0.0142108734
Maximum0.01535294718
Range0.008778940233
Interquartile range (IQR)0.002616331693

Descriptive statistics

Standard deviation0.001800492767
Coefficient of variation (CV)0.1660074824
Kurtosis-0.1948141859
Mean0.01084585309
Median Absolute Deviation (MAD)0.001204267679
Skewness0.4456281725
Sum1.637723817
Variance3.241774204 × 10-6
MonotonicityNot monotonic
2022-03-10T11:21:50.492227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.010183861471
 
0.7%
0.011015736931
 
0.7%
0.0103785921
 
0.7%
0.010607366671
 
0.7%
0.011710021131
 
0.7%
0.0095644510151
 
0.7%
0.01108074891
 
0.7%
0.012912024421
 
0.7%
0.015231318981
 
0.7%
0.0098446081131
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
0.006574006951
0.7%
0.0075751276241
0.7%
0.0077380789831
0.7%
0.007849031411
0.7%
0.0079837782481
0.7%
0.0081685083871
0.7%
0.0081723732131
0.7%
0.0081741202141
0.7%
0.0083172014181
0.7%
0.0083785567261
0.7%
ValueCountFrequency (%)
0.015352947181
0.7%
0.015247761161
0.7%
0.015231318981
0.7%
0.015224817411
0.7%
0.015066536681
0.7%
0.014467974651
0.7%
0.01438499871
0.7%
0.01425456781
0.7%
0.0141671791
0.7%
0.013809841631
0.7%

ifm_overlap_em_non-zero_voxels_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean69480.17881
Minimum34360
Maximum110795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:50.602839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34360
5-th percentile50353.5
Q158459
median67670
Q379006.5
95-th percentile93870
Maximum110795
Range76435
Interquartile range (IQR)20547.5

Descriptive statistics

Standard deviation14246.48389
Coefficient of variation (CV)0.2050438577
Kurtosis-0.006512655234
Mean69480.17881
Median Absolute Deviation (MAD)9928
Skewness0.4124971728
Sum10491507
Variance202962303.3
MonotonicityNot monotonic
2022-03-10T11:21:50.714944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
833761
 
0.7%
912681
 
0.7%
569081
 
0.7%
663281
 
0.7%
747731
 
0.7%
697881
 
0.7%
700671
 
0.7%
666891
 
0.7%
526221
 
0.7%
554441
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
343601
0.7%
396191
0.7%
413281
0.7%
470251
0.7%
477901
0.7%
482751
0.7%
494581
0.7%
503131
0.7%
503941
0.7%
513711
0.7%
ValueCountFrequency (%)
1107951
0.7%
1061831
0.7%
1037171
0.7%
1033871
0.7%
1011361
0.7%
969071
0.7%
953481
0.7%
953001
0.7%
924401
0.7%
920151
0.7%

ifm_overlap_em_non-zero_voxels_volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean76822.55373
Minimum34360
Maximum110772.1769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:50.830774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34360
5-th percentile56583
Q169266.5
median74773
Q384612.77554
95-th percentile100574.0705
Maximum110772.1769
Range76412.17693
Interquartile range (IQR)15346.27554

Descriptive statistics

Standard deviation12447.32252
Coefficient of variation (CV)0.1620269299
Kurtosis0.8101327264
Mean76822.55373
Median Absolute Deviation (MAD)7103
Skewness0.1308644587
Sum11600205.61
Variance154935838
MonotonicityNot monotonic
2022-03-10T11:21:50.945054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700671
 
0.7%
953481
 
0.7%
78234.096361
 
0.7%
912681
 
0.7%
569081
 
0.7%
792711
 
0.7%
747731
 
0.7%
71767.983151
 
0.7%
833761
 
0.7%
81792.861391
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
343601
0.7%
47034.328081
0.7%
503131
0.7%
525791
0.7%
52886.238461
0.7%
55167.532321
0.7%
561081
0.7%
562581
0.7%
569081
0.7%
586181
0.7%
ValueCountFrequency (%)
110772.17691
0.7%
106646.81721
0.7%
1061831
0.7%
105110.38111
0.7%
1037171
0.7%
1033871
0.7%
102522.0671
0.7%
101115.16661
0.7%
100032.97431
0.7%
98405.326061
0.7%

pc_ifm_overlap_em_voxels
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct151
Distinct (%)100.0%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.005781753563
Minimum0.002828868754
Maximum0.009102373461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-03-10T11:21:51.216939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.002828868754
5-th percentile0.004223993401
Q10.004921803102
median0.005658160913
Q30.006472593665
95-th percentile0.007911087615
Maximum0.009102373461
Range0.006273504707
Interquartile range (IQR)0.001550790564

Descriptive statistics

Standard deviation0.001149412179
Coefficient of variation (CV)0.1987999258
Kurtosis0.1483556238
Mean0.005781753563
Median Absolute Deviation (MAD)0.0008094926264
Skewness0.4516650088
Sum0.8730447881
Variance1.321148358 × 10-6
MonotonicityNot monotonic
2022-03-10T11:21:51.327811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0051617333841
 
0.7%
0.0068607337781
 
0.7%
0.0079776625451
 
0.7%
0.0073726842471
 
0.7%
0.0055637552521
 
0.7%
0.0054542371551
 
0.7%
0.0057615149481
 
0.7%
0.0079152707081
 
0.7%
0.0045505989851
 
0.7%
0.0064662162211
 
0.7%
Other values (141)141
92.8%
ValueCountFrequency (%)
0.0028288687541
0.7%
0.003388569031
0.7%
0.0034726070471
0.7%
0.0038008919431
0.7%
0.0040326874681
0.7%
0.0040910337421
0.7%
0.0041721400451
0.7%
0.0042014919041
0.7%
0.0042464948971
0.7%
0.0042560714321
0.7%
ValueCountFrequency (%)
0.0091023734611
0.7%
0.0088061764571
0.7%
0.0085934687581
0.7%
0.0085479862741
0.7%
0.0083726345591
0.7%
0.0081036211871
0.7%
0.0079776625451
0.7%
0.0079152707081
0.7%
0.0079069045221
0.7%
0.0076147758421
0.7%

Interactions

2022-03-10T11:21:45.601173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.162195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.322576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.543869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.604951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.839414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.866042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.019263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.060218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.263234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.201462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.398811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.682449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.410931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.403533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.632046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.690523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.920467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.945087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.097209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.335906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.337403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.452333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.480928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.767853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.495713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.491582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.720133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.984719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.008069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.197112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.194364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.423198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.417951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.543184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.571006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.849314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.576678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.763858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.803099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.070237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.092293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.278944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.278546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.504267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.493925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.626945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.655451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.930938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.656913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.848304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.888119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.153381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.175346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.359447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.364009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.585394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.570668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.710607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.739279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.013170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.737428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.931603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.975905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.236248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.270798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.440036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.447121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.667106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.648263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.794986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.823313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.096247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.818386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.014684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.061012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.321561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.355262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.521074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.527949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.753662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.725776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.879952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.907959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.178724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.899933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.101008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.147652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.406403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.439764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.602705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.609003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.837225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.803038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.964218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.993027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.264472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:33.983068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.188087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.237276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.494334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.529068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.687075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.727200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:41.923348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.884324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.052541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.081056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.348713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.059995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.267113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.318416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.572576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.607356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.762163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.802739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.001850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.955317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.131226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.160672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.438569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.146157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.357505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.418873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.663223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.694606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.849057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.889187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.090380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.039177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.221324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.252446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:46.526572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:34.230794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:35.448648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:36.512660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:37.751228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:38.780629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:39.934769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:40.974953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:42.176945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:43.120733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:44.310308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-10T11:21:45.509019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-10T11:21:51.439315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-10T11:21:51.626900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-10T11:21:51.812450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-10T11:21:51.979747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-10T11:21:52.100407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-10T11:21:46.721145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-10T11:21:46.939242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-10T11:21:47.280412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-10T11:21:47.536841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

subject_idacqbm_non-zero_voxels_numberbm_non-zero_voxels_volumebm_minus_ifm_non-zero_voxels_before_plugging_numberbm_minus_ifm_non-zero_voxels_before_plugging_volumebm_minus_ifm_non-zero_voxels_after_plugging_numberbm_minus_ifm_non-zero_voxels_after_plugging_volumeifm_overlap_dil_em_non-zero_voxels_numberifm_overlap_dil_em_non-zero_voxels_volumeifm_non-zero_voxelsifm_non-zero_volumepc_ifm_overlap_dil_em_voxelsifm_overlap_em_non-zero_voxels_numberifm_overlap_em_non-zero_voxels_volumepc_ifm_overlap_em_voxels
0sub-ON01016fspgr1125616.01502551.7501269.4620361269.4620360.00.076863.0102602.15923611691956.015607248.00.00657439619.052886.2384600.003389
1sub-ON01802mprage1537842.01537842.00095.00000095.0000000.00.0149049.0149049.00000012195830.012195830.00.01222183376.083376.0000000.006836
2sub-ON02693mprage1648092.01648092.00098.00000098.0000000.00.0104714.0104714.00000012191133.012191133.00.00858958618.058618.0000000.004808
3sub-ON02747mprage1332726.01332726.00096.00000096.0000000.00.0105423.0105423.00000012202544.012202544.00.00863956258.056258.0000000.004610
4sub-ON02811mprage1321127.01321127.00074.00000074.0000000.00.0115450.0115450.00000012360166.012360166.00.00934063023.063023.0000000.005099
5sub-ON03748mprage1636853.01636853.00038.00000038.0000000.00.0120862.0120862.00000012178271.012178271.00.00992469535.069535.0000000.005710
6sub-ON05258mprage1369792.01368723.75032.97426632.9742660.00.0129704.0129602.8481089815328.09807673.00.01321462437.062388.3074330.006361
7sub-ON05311fspgr1196691.01597427.6251811.4193121811.4193120.00.0104563.0139578.07496711791195.015739720.00.00886853657.071625.1519990.004551
8sub-ON05530mprage1718025.01718025.000118.000000118.0000002.02.0106584.0106584.00000012357223.012357223.00.00862559335.059335.0000000.004802
9sub-ON06910fspgr1075407.01435529.2501313.5126951313.5126950.00.0106099.0141628.43621511707839.015628450.00.00906254195.072343.3123840.004629

Last rows

subject_idacqbm_non-zero_voxels_numberbm_non-zero_voxels_volumebm_minus_ifm_non-zero_voxels_before_plugging_numberbm_minus_ifm_non-zero_voxels_before_plugging_volumebm_minus_ifm_non-zero_voxels_after_plugging_numberbm_minus_ifm_non-zero_voxels_after_plugging_volumeifm_overlap_dil_em_non-zero_voxels_numberifm_overlap_dil_em_non-zero_voxels_volumeifm_non-zero_voxelsifm_non-zero_volumepc_ifm_overlap_dil_em_voxelsifm_overlap_em_non-zero_voxels_numberifm_overlap_em_non-zero_voxels_volumepc_ifm_overlap_em_voxels
142sub-ON95259mprage1364801.01364801.00032.00000032.0000000.00.092009.092009.00000012146198.012146198.00.00757534360.034360.0000000.002829
143sub-ON95422mprage1736716.01736716.000100.000000100.0000000.00.0155709.0155709.00000012090255.012090255.00.01287988512.088512.0000000.007321
144sub-ON95742mprage1567437.01567437.000101.000000101.0000000.00.0128010.0128010.00000012334043.012334043.00.01037969788.069788.0000000.005658
145sub-ON97504mprage1444342.01444342.00067.00000067.0000000.00.0130238.0130238.00000012366555.012366555.00.01053168364.068364.0000000.005528
146sub-ON98018fspgr1190990.01589817.5001763.3640141763.3640140.00.0114452.0152778.61037011712355.015634479.00.00977261274.081792.8613900.005232
147sub-ON98098mprage1346049.01346049.00065.00000065.0000000.00.0123050.0123050.00000012387861.012387861.00.00993366888.066888.0000000.005399
148sub-ON98806mprage1581403.01581403.00061.00000061.0000000.00.0114561.0114561.00000012107623.012107623.00.00946256908.056908.0000000.004700
149sub-ON99299fspgr1043518.01392961.3751209.3928221209.3928220.00.0105665.0141049.10237311654062.015556665.00.00906755444.074010.5657690.004757
150sub-ON99620mprage1346140.01346140.00040.00000040.0000000.00.0135155.0135155.00000012191289.012191289.00.01108673510.073510.0000000.006030
151NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN